Smart home inhabitants can specify trigger-condition-action rules to control the home's behavior. As the number of rules and their complexity grow, however, so does the probability of issues such as inconsistencies and redundancies. These can lead to unintended behavior, including security vulnerabilities and wasted resources, which harms the inhabitants' trust in the system. Existing approaches to handle unintended behavior typically require inhabitants to define all-encompassing, permanent solutions by modifying the rules. Although this is fitting in certain situations, some unforeseen situations might occur. We argue that the user always must have the last word to avoid unwanted behaviors, without altering the overall behavior. With FortClash, we present an approach to predict many different types of unintended behavior, and contribute four novel mechanisms to mediate them that rely on making one-time exceptions. With FortClash, inhabitants gain a new tool to deal with unintended behavior in the short-term that is compatible with existing long-term approaches such as editing rules.

The Engineering Interactive Computing Systems (EICS) track of the Proceedings of the ACM on Human-Computer Interaction (PACM-HCI) is the primary venue for research contributions at the intersection of Human-Computer Interaction (HCI) and Software Engineering. EICS 2022 is the fourteenth edition of the EICS conference, however, our community was the first to organize a scientific gathering to foster and exchange research ideas and contributions on how to engineer the effective interactive aspects of a computing system. In the seventies of the previous century, the Conference on Command Languages explored the emerging primary technologies to interact with computing systems, namely command languages. Since then, this conference has evolved into the Engineering HCI conference, and the same community organized sibling conferences such as CADUI (Computer-Aided Design of User Interfaces), Tamodia (Tasks, Models and Diagrams) and DSV-IS (Design Specification and Verification of Interactive Systems). These separate venues merged into one single ACM SIGCHI sponsored conference in 2010 EICS (see Fig.1). This conference became the primary venue for rigorous contributions, and dissemination of research results, that hold the interconnection between user interface design, software engineering and computational interaction.

Within our work, we apply context-awareness to determine how AR/VR technology should adapt instructions based on the context to suit user needs. We focus on situations where the user must carry out a complex manual activity that requires additional information to be present during the activity to achieve the desired result. To this end, the emphasis is on activities that require fine-motor skills and in-depth expertise and training, for which XR is a powerful tool to support and guide users performing these tasks. The contexts we detect include user intentions, environmental conditions, and activity progressions. Our work builds on these contexts with the main focus on determining how XR should adapt for the end-user from a usability perspective. The feedback we request from ISMAR consists of input in detection, usability, and simulation categories, together with how to balance these categories to create real-time and user-friendly systems. The next steps of our work will consider how to content should adjust based on the cognitive load, activity space, and environmental conditions.

As robots are equipped with software that makes them increasingly autonomous, it becomes harder for humans to understand and control these robots. Human users should be able to understand and, to a certain amount, predict what the robot will do. The software that drives a robotic system is often very complex, hard to understand for human users, and there is only limited support for ensuring robotic systems are also intelligible. Adding intelligibility to the behavior of a robotic system improves the predictability, trust, safety, usability, and acceptance of such autonomous robotic systems. Applying intelligibility to the interface design can be challenging for developers and designers of robotic systems, as they are expert users in robot programming but not necessarily experts on interaction design. We propose Choreobot, an interactive, online, and visual dashboard to use with our reference framework to help identify where and when adding intelligibility to the interface design is required, desired, or optional. The reference framework and accompanying input cards allow developers and designers of robotic systems to specify a usage scenario as a set of actions and, for each action, capture the context data that is indispensable for revealing when feedforward is required. The Choreobot interactive dashboard generates a visualization that presents this data on a timeline for the sequence of actions that make up the usage scenario. A set of heuristics and rules are included that highlight where and when feedforward is desired. Based on these insights, the developers and designers can adjust the interactions to improve the interaction for the human users working with the robotic system.

Feedback and feedforward are two fundamental mechanisms that support users' activities while interacting with computing devices. While feedback can be easily solved by providing information to the users following the triggering of an action, feedforward is much more complex as it must provide information before an action is performed. For interactive applications where making a mistake has more impact than just reduced user comfort, correct feedforward is an essential step toward correctly informed, and thus safe, usage. Our approach, Fortunettes, is a generic mechanism providing a systematic way of designing feedforward addressing both action and presentation problems. Including a feedforward mechanism significantly increases the complexity of the interactive application hardening developers' tasks to detect and correct defects. We build upon an existing formal notation based on Petri Nets for describing the behavior of interactive applications and present an approach that allows for adding correct and consistent feedforward.

Virtual Reality (VR) allows simulation of machine control panels without physical access to the machine, enabling easier and faster initial exploration, testing, and validation of machine panel designs. However, haptic feedback is indispensable if we want to interact with these simulated panels in a realistic manner. We present HapticPanel, an encountered-type haptic system that provides realistic haptic feedback for machine control panels in VR. To ensure a realistic manipulation of input elements, the user's hand is continuously tracked during interaction with the virtual interface. Based on which virtual element the user intends to manipulate, a motorized panel with stepper motors moves a corresponding physical input element in front of the user's hand, enabling realistic physical interaction.

The promise of on-body interactions has led to widespread development of wearable displays. They manifest themselves in highly variable shapes and form, and are realized using technologies with fundamentally different properties. Through an extensive survey of the field of wearable displays, we characterize existing systems based on key qualities of displays and wearables, such as location on the body, intended viewers or audience, and the information density of rendered content. We present the results of this analysis in an open, web-based interactive design space that supports exploration and refinement along various parameters. The design space, which currently encapsulates 129 cases of wearable displays, aims to inform researchers and practitioners on existing solutions and designs, and enable the identification of gaps and opportunities for novel research and applications. Further, it seeks to provide them with a thinking tool to deliberate on how the displayed content should be adapted based on key design parameters. Through this work, we aim to facilitate progress in wearable displays, informed by existing solutions, by providing researchers with an interactive platform for discovery and reflection.

We present Rataplan, a robust and resilient pixel-based approach for linking multi-modal proxies to automated sequences of actions in graphical user interfaces (GUIs). With Rataplan, users demonstrate a sequence of actions and answer human-readable follow-up questions to clarify their desire for automation. After demonstrating a sequence, the user can link a proxy input control to the action which can then be used as a shortcut for automating a sequence. Alternatively, output proxies use a notification model in which content is pushed when it becomes available. As an example use case, Rataplan uses keyboard shortcuts and tangible user interfaces (TUIs) as input proxies, and TUIs as output proxies. Instead of relying on available APIs, Rataplan automates GUIs using pixel-based reverse engineering. This ensures our approach can be used with all applications that offer a GUI, including web applications. We implemented a set of important strategies to support robust automation of modern interfaces that have a flat and minimal style, have frequent data and state changes, and have dynamic viewports.

In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual search, and a computational approach to restructure graphical layouts for an individual user such that features on a new, unvisited interface can be found quicker. It explores four technical principles inspired by the human visual system (HVS) to predict expected positions of features and create individualised layout templates: (I) the interface with highest frequency is chosen as the template; (II) the interface with highest predicted recall probability (serial position curve) is chosen as the template; (III) the most probable locations for features across interfaces are chosen (visual statistical learning) to generate the template; (IV) based on a generative cognitive model, the most likely visual search locations for features are chosen (visual sampling modelling) to generate the template. Given a history of previously seen interfaces, we restructure the spatial layout of a new (unseen) interface with the goal of making its features more easily findable. The four HVS principles are implemented in Familiariser, a web browser that automatically restructures webpage layouts based on the visual history of the user. Evaluation of Familiariser (using visual statistical learning) with users provides first evidence that our approach reduces visual search time by over 10

The number of wearable devices that we carry increases, with smaller companion devices like smartwatches providing quick access for simple tasks. These devices are, however, not necessarily in direct sight of the user and during everyday activities, it is unlikely, even undesirable, that the user constantly focuses on or interacts with these screens. Furthermore, interaction is often limited because our hands are occupied carrying or holding items such as bags, papers, boxes, or tools. In this paper, we evaluate how encumbrance affects, among others, the time it takes to perceive and react to a notification depending on the placement of the companion device. Our experimental results can assist designers in choosing the right device for the task.